Actor-Critic Reward Transfer Using Affine Critic Adjustment
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Solution Overview
Problem
Reinforcement learning models face turbulence when transitioning between different reward systems or datasets, leading to wildly disparate Q-values and inefficient training.
Innovation Solution
Applying a minimally invasive affine transformation layer to the critic model during the transition, allowing for a smooth shift in evaluation before retraining with the new reward system or dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the reward system is transferred directly from one system to another in an actor-critic model, then the model can adapt to new environments or tasks, but the Q-values become wildly disparate and training becomes inefficient due to turbulence
Solution Approach 1:
The patent introduces an intermediary mechanism (reward scaling factor and gradual transition schedule) between the old and new reward systems. This intermediary gradually adjusts the reward signal during a transition period, preventing direct discontinuous changes that cause turbulence. The scaling factor acts as a mediator that smoothly blends the old and new reward systems, allowing the model to adapt without wild Q-value fluctuations while maintaining training efficiency.
Solution Approach 2:
The patent changes the reward system parameters gradually rather than making abrupt changes. By implementing a transition schedule that progressively modifies the reward scaling factor over time, the system allows smooth parameter evolution. This gradual parameter change prevents sudden shifts in Q-values and maintains training stability while enabling adaptation to new reward systems.
2Adaptability or versatility
If the reward system is transferred directly from one system to another in an actor-critic model, then the model can adapt to new environments or tasks, but training volatility increases significantly
Solution Approach 1:
The patent introduces an intermediary mechanism (reward scaling factor and gradual transition schedule) between the old and new reward systems. This intermediary gradually adjusts the reward signal during a transition period, preventing direct discontinuous changes that cause turbulence. The scaling factor acts as a mediator that smoothly blends the old and new reward systems, allowing the model to adapt without wild Q-value fluctuations while maintaining training stability.
Solution Approach 2:
The patent implements beforehand cushioning by introducing a gradual transition schedule that prepares the model for reward system changes. The transition schedule预先 establishes a cushioning period where the reward scaling factor is progressively adjusted, preventing sudden shocks to the training process. This prior cushioning reduces training volatility by absorbing the impact of reward system changes over time rather than all at once.
Data Source
AI summary
Methods and systems for smoothening the transition of reward systems or datasets for actor-critic reinforcement learning models. A reinforcement model such as an actor-critic model is trained on a first dataset and a first reward system. The weights of the actor model and the critic model are frozen. While these weights are frozen, an affine transformation layer is attached to a final layer of the critic model, and the affine transformation layer is trained with a second dataset and a second reward system in order to adjust a weight of the final layer of the critic model. Then, the weights of the critic model are unfrozen which allows the adjusted weight of the final layer of the critic model to be implemented. The reinforcement learning model is retrained on the second dataset and second reward system, first with just the critic weights unfrozen, and then with both actor and critic weights unfrozen.